
'''simple docstring'''

from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class       UpperCAmelCase					(       __lowerCAmelCase			):





		"""simple docstring"""





		A__	: Optional[int]    =     ['image_processor', 'tokenizer']
		A__	: Optional[int]    =     'BridgeTowerImageProcessor'
		A__	: Optional[int]    =     ('RobertaTokenizer', 'RobertaTokenizerFast')
		def __init__(     self	,       _snake_case	,       _snake_case  )					->     List[Any]:
				super().__init__(lowerCamelCase_	,       lowerCamelCase_  )
		def __call__(     self	,       _snake_case	,       _snake_case = None	,       _snake_case = True	,       _snake_case = False	,       _snake_case = None	,       _snake_case = None	,       _snake_case = 0	,       _snake_case = None	,       _snake_case = None	,       _snake_case = None	,       _snake_case = False	,       _snake_case = False	,       _snake_case = False	,       _snake_case = False	,       _snake_case = True	,       _snake_case = None	,       **_snake_case	,       )					->     Optional[int]:
				_UpperCamelCase							:			Tuple     =   self.tokenizer(
				    text=lowerCamelCase_	,       add_special_tokens=lowerCamelCase_	,       padding=lowerCamelCase_	,       truncation=lowerCamelCase_	,       max_length=lowerCamelCase_	,       stride=lowerCamelCase_	,       pad_to_multiple_of=lowerCamelCase_	,       return_token_type_ids=lowerCamelCase_	,       return_attention_mask=lowerCamelCase_	,       return_overflowing_tokens=lowerCamelCase_	,       return_special_tokens_mask=lowerCamelCase_	,       return_offsets_mapping=lowerCamelCase_	,       return_length=lowerCamelCase_	,       verbose=lowerCamelCase_	,       return_tensors=lowerCamelCase_	,       **lowerCamelCase_	,       )
				# add pixel_values + pixel_mask
				_UpperCamelCase							:			int     =   self.image_processor(
				    lowerCamelCase_	,       return_tensors=lowerCamelCase_	,       do_normalize=lowerCamelCase_	,       do_center_crop=lowerCamelCase_	,       **lowerCamelCase_  )
				encoding.update(lowerCamelCase_  )

				return encoding
		def _lowercase	(     self	,       *_snake_case	,       **_snake_case  )					->     Union[str, Any]:
				return self.tokenizer.batch_decode(*lowerCamelCase_	,       **lowerCamelCase_  )
		def _lowercase	(     self	,       *_snake_case	,       **_snake_case  )					->     Dict:
				return self.tokenizer.decode(*lowerCamelCase_	,       **lowerCamelCase_  )




		@property
		def _lowercase	(     self  )					->     List[Any]:
				_UpperCamelCase							:			int     =   self.tokenizer.model_input_names
				_UpperCamelCase							:			List[Any]     =   self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )






def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	list:







 '''simple docstring'''
 UpperCamelCase			  =      len(UpperCamelCase_       )
 UpperCamelCase			  =      [[0] * n for i in range(UpperCamelCase_       )]
 for i in range(UpperCamelCase_       ):
  UpperCamelCase			  =      y_points[i]

 for i in range(2							,					UpperCamelCase_       ):
  for j in range(UpperCamelCase_							,					UpperCamelCase_       ):
   UpperCamelCase			  =      (
       (xa - x_points[j - i + 1]) * q[j][i - 1]
       - (xa - x_points[j]) * q[j - 1][i - 1]
   ) / (x_points[j] - x_points[j - i + 1])

 return [q[n - 1][n - 1], q]


if __name__ == "__main__":
     import doctest

     doctest.testmod()
